Programista Back-End

Linde Material Handling
2 days ago

Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Working hours
Regular working hours
Languages
English

Job location

Tech stack

Artificial Intelligence
Automation of Tests
Azure
Python
Machine Learning
TensorFlow
Management of Software Versions
PyTorch
Large Language Models
Generative AI
Build Management
Containerization
Scikit Learn
Kubernetes
Information Technology
Data Analytics
Machine Learning Operations
Virtual Agents
Code Restructuring

Requirements

Linde Material Handling is among the world leaders in the manufacturing of forklifts, warehouse trucks, and high-performance solutions for intralogistics. COURAGE - we drive change and innovation You will work for a successful global company in an international environment. About the company Extensive trainings in your area of responsibility. The opportunity to participate in the international projects and a significant influence on company IT development. Hybrid Work Model. Flexible Working Hours. Private health insurance. Design, build and deploy innovative solutions in the area of data analytics, machine learning and generative AI to solve complex real-world problems for our internal and external customers. Apply machine learning and generative AI models for end-to-end solution building. Build MLOps on cloud (Azure and GCP) and build CI/CD pipelines. AI model review, code refactoring, containerization, deployment, versioning and monitoring. AI model validation, test automation and integration. Collaborate closely in interdisciplinary teams with data scientists, data engineers, business units, UX designers and IT departments. Completed studies in computer science, mathematics, physics or a comparable education. ~Ability to design, implement and maintain AI-driven applications on cloud (Azure and GCP), including performance and cost monitoring and optimization. ~ Ability to build data and MLOps pipelines and experience with MLOps frameworks (e.g., MLflow) and with Kubernetes. ~ Experience with leveraging Retrieval Augmented Generation (RAG) architectures and monitoring them in production using LLM Observability platforms (e.g., Excellent Python programming skills and familiarity with relevant frameworks and libraries like scikit-learn, Pytorch, Tensorflow etc. ~ Fluent in English.

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